Processor implemented systems and methods for measuring cognitive abilities

ABSTRACT

A computer-implemented cognitive assessment tool is provided for assessing cognitive ability of an individual while multi-tasking. In one embodiment, a computer processing system on which the tool is implemented may receive form the individual first responses to a first task and second responses to a second task, where the first task and the second task are presented to the individual simultaneously. The system may determine that the first task and the second task are performed by the individual based on the first responses and the second responses, and compute a cognitive measure using one or both of the first responses and the second responses. Further, computing the cognitive measure may be based on performance measures of one or both of the first responses and the second responses. Based on the cognitive measure, the system may output a cognitive assessment to the individual.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/068,061 filed Mar. 11, 2016, which claims the benefit of U.S.Provisional Application No. 62/132,009 filed Mar. 12, 2015; the entiretyof each of these applications are hereby incorporated herein byreference.

FIELD

The disclosed embodiments generally relates to a computer-implementedmethods for measuring cognitive function of an individual.

BACKGROUND

Cognitive function is recognized as an informative marker of manydisease processes such as dementia, depression, Autism SpectrumDisorder, Attention Deficit Hyperactivity Disorder, and even healthyaging. For this reason, monitoring cognitive function has become animportant part of an individual's screening, medical diagnosis,monitoring of therapy, and investigation into the emerging cognitivetraining field.

SUMMARY

Conventional validated cognitive assessment tools have a few problems.

The primary issue is that the cognitive assessment process are tediousfor a user to perform. The un-engaging tasks and interface do not createthe environment in which every user performs to his or her highestabilities, consequently giving inaccurate scores and normative datasets. Additionally, users may be unwilling to comply with a request toperform the same assessment process multiple times.

The second issue with current cognitive assessment processes is thatthey can be time consuming for both the user and the person evaluatingthe user's performance. In some cases this leads to the decision to notundergo cognitive evaluation, even though the information provided couldbe of value. It also makes it logistically difficult to perform the samecognitive evaluation on one person many times to understand how his orher cognitive function is changing over time.

Finally, currently available cognitive assessments are insensitive toknown cognitive deficits in populations. It is to be appreciated thatthere is no known currently available and commonly used cognitive toolscapable of distinguishing a population with chromosomal abnormality in16p.11.2 BP4-BP5 that causes behavioral and cognitive symptoms from agroup of age-matched siblings.

Thus, a new cognitive assessment tool would be useful if it could detectdeficits that are not optimally identified by current tools and areengaging or seamlessly incorporated into everyday tasks. This presentdisclosure describes a unique computer-implemented cognitive assessmenttool that evaluates user inputs when a user is performing at least twotasks simultaneously. This new tool may be enabled by computers becausecomputers allow two tasks to be presented, adapted, and evaluatedsimultaneously, which is something humans cannot achieve with fidelityand reliability. The methods of this disclosure can be used in medical,educational, and professional settings. The cognitive assessment tooldescribed below can be used as a one-time evaluation or given to two ormore times for monitoring purpose without significant strain on the useror the person evaluating the cognitive function.

The purpose and advantages of the below described illustratedembodiments will be set forth in and apparent from the description thatfollows. Additional advantages of the illustrated embodiments will berealized and attained by the devices, systems and methods particularlypointed out in the written description and claims hereof, as well asfrom the appended drawings.

Measuring and understanding cognitive function is important in manyareas: diagnosing a disease, diagnosing a neurological condition,monitoring response to and side effects of medical interventions, andaddressing educational placements and needs. However, current cognitivemeasurement tools are either too time consuming to be performed on aregular basis, have poor compliance and adherence because they do notkeep the user engaged, or can be insensitive to known differences incognitively impaired populations. The present disclosure provides anovel cognitive assessment that measures performance through user inputsto a computer device while the user is performing at least two tasks atonce (“multi-tasking”).

While other cognitive assessments may rely on user inputs tocomputational devices, most rely on a participant performing only onetask at a time, or having a second task be present but not meant to beperformed (for example, a distraction to be ignored). The existingcognitive assessment method that does incorporate multi-taskingspecifically focuses the method on highlighting the difference betweenthe multi-tasking phase versus separate performance of the single-taskcomponents in isolation, and finds that the utility of the method liessolely in calculating the performance cost of being in a multi-taskingenvironment by comparing the multi-task performance to when the task isperformed by itself. A unique aspect of the present disclosure is thatit finds that performance data collected in the multi-taskingenvironment, in addition to the previously useful multi-tasking “cost”data, can be specifically informative of a user's cognitive state and insome cases more sensitive than the cost data.

The methods described are implemented on a computer device with an inputcomponent. The computer device enables the methods because it allows forthe presentation of two tasks and measurement of user responses to thetwo tasks simultaneously, which is something humans are not capable ofdoing with fidelity and reliability. The computer device also allow forthe adaption of difficulty of both tasks independently. Additionally,without the temporal resolution that the computer device is able toprovide, the performance measurements would be not be effectivecognitive measures.

The present disclosure describes computer-implemented methods formeasuring cognitive ability or function of an individual, wherein themethod may be implemented using a computer device having an inputcomponent. Measurements may be taken while the user is performing atleast two distinct tasks (“multi-tasking”), each of which requiring aninput to the computer device. The computer may perform an analysis ofthe performance measures of at least one of the tasks based on acognitive measure, and based on the cognitive measure output anassessment indicative of cognitive ability or function of the user.

This method can be implemented in multiple scenarios, includingmeasurement of performance when the user engages in two or more taskssimultaneously for purposes other than cognitive measurement (passivetasks) and active measurement of performance on prescribed tasksspecifically designed for cognitive assessment (active tasks). Examplesof passive tasks are: writing emails, responding to instant messages,and browsing the internet. Active tasks are those that are designed toevaluate a user's cognitive function in a specific domain, such asmemory task. Commercially available video games often engage users inmulti-tasking and offer an excellent opportunity for cognitiveevaluation. Additionally, video games may be specifically designed topresent multi-tasking with active tasks.

In one aspect, the user inputs in a multi-task environment may beanalyzed based on different performance measures. Among them are theperformance threshold for which a certain accuracy can be maintained,the mean performance over a period of time, the variation in theperformance level over time, the reaction time to certain stimuli, thevariation in the reaction time, and the ability to differentiate betweeninterference stimuli to which a user should respond and distractorstimuli which should be ignored. These performance measures can beanalyzed using standard techniques, combined to create compositevariables, and measured over time to provide additional cognitivemeasures.

The methods of this disclosure can be used, among other things, todiagnose cognitive deficits, to help diagnose specific disease states,to monitor response to a therapy, to monitor for side effects intherapies known to cause cognitive side effects or those with unknownpharmacodynamics, and to help in educational assessments and placements.

In the described illustrated embodiments, presented are specificembodiments of the deployment, testing, and efficacy of this newapproach in various clinical populations. In some illustratedembodiments, the computer-based cognitive assessment tools areimplemented in a video game that presents two active cognitive taskssimultaneously. In a few particularly illustrated embodiments, thecomputer-based methods are implemented in a video game that presents avisuomotor task and a perceptual reaction task simultaneously. Theillustrated embodiment can be tested to show known cognitive decline inaging populations, differentiate between populations with more accuracythan traditional cognitive measures, differentiation between differentclinical populations, and show stability of the tool's measurements overtime.

For example, the present disclosure provides various exemplaryembodiments of the cognitive assessment tool described above. In oneembodiments, the computer-implemented method for assessing cognitiveability of an individual is implemented using a hand-held computingdevice having a display component, an input device, and a sensor. Themethod comprises: presenting, by the display component, a visuomotortask to the individual over a period of time, the visuomotor taskincluding a navigation path evoking navigation responses from theindividual; presenting, by the display component, a reaction task to theindividual over the period of time, the reaction task including targetstimuli evoking reaction responses from the individual and distractorstimuli that require no response from the individual, wherein thestimuli are presented simultaneously with at least some of thenavigation path; receiving the navigation responses using the sensor;receiving the reaction responses using the input device; determining, bythe hand-held computing device, that the visuomotor task is beingperformed by the individual based on the navigation responses;computing, by the hand-held computing device, a cognitive measure usingthe reaction responses; outputting, by the hand-held computing device, acognitive assessment based on the cognitive measure.

In one embodiment, the present disclosure provides acomputer-implemented method for assessing cognitive ability of anindividual. In one embodiment, the computer-implemented methodcomprises: receiving, by a computer processing system, a first pluralityresponses by the individual to a first task, the first task includingfirst stimuli evoking the first plurality of responses from theindividual over a period of time; receiving, by the computer processingsystem, a second plurality of responses by the individual to a secondtask, the second task including second stimuli evoking the secondplurality of responses from the individual over the period of time,wherein the second stimuli are presented simultaneously with at leastsome of the first stimuli; determining, by the computer processingsystem, that the first task and the second task are performed by theindividual based on the first plurality of responses and the secondplurality of responses; computing, by the computer processing system, acognitive measure using one or both of the first plurality of responsesand the second plurality of responses; and outputting, by the computerprocessing system, a cognitive assessment based on the cognitivemeasure.

In related examples of embodiments, computing the cognitive measureincludes determining performance measures using one or both of the firstplurality of responses and the second plurality of responses.

In related examples of embodiments, the performance measures areselected from the group consisting of: reaction time of responses andcorrectness of responses.

In related examples of embodiments, one or both of the first pluralityof responses and the second plurality of responses are detected usingone or more sensors, the sensors being selected from the groupconsisting of: accelerometer and gyroscope.

In related examples of embodiments, the computer-implemented method canfurther include the steps of: determining performance measures using oneor both of the first plurality of responses and the second plurality ofresponses; and modifying, during the period of time, a difficulty levelof the first task or the second task based on performance measures.

In related examples of embodiments, the difficulty level corresponds toa game level.

In related examples of embodiments, the difficulty level is selectedfrom the group consisting of: allowable reaction time window forreacting to stimuli, navigation speed, number of obstacles, size ofobstacles, frequency of turns in a navigation path, and turning radiusesof turns in a navigation path.

In related examples of embodiments, the difficulty level is modified inreal-time during the period of time; and wherein the cognitive measureis computed using the difficulty level modifications made during theperiod of time.

In related examples of embodiments, the computer-implemented method canfurther include the steps of: determining a threshold of the difficultylevel at which the performance measures satisfy one or morepredetermined criteria; wherein the cognitive measure is computed usingthe determined threshold of the difficulty level.

In related examples of embodiments, the one or more predeterminedcriteria include maintaining a predetermined level of performance over apredetermined amount of time.

In related examples of embodiments, the computer-implemented method canfurther include the steps of: modifying, during the period of time, afirst difficulty level of the first task based on performance measuresof one or both of the first plurality of responses and the secondplurality of responses; and modifying, during the period of time, asecond difficulty level of the second task based on performance measuresof one or both of the first plurality of responses and the secondplurality of responses; wherein the first difficulty level and thesecond difficulty level are modified in real-time during the period oftime; and wherein the cognitive measure is computed using one or both ofthe first difficulty level modifications and the second difficulty levelmodifications.

In related examples of embodiments, the first task is a visuomotor task,the first stimuli include a navigation path, and the first plurality ofresponses include continuous inputs.

In related examples of embodiments, the second task is a reaction task,the second stimuli include target stimuli that require responses fromthe individual, and the second plurality of responses include inputsreacting to the interferences.

In related examples of embodiments, the second stimuli includedistractor stimuli that require no response from the individual.

In related examples of embodiments, computing the cognitive measureincludes applying statistical analysis to one or both of the firstplurality of responses and the second plurality of responses.

In related examples of embodiments, computing the cognitive measureincludes comparing the performance measures to predetermined performancemeasures representative of individuals with known cognitive conditions.

In related examples of embodiments, computing the cognitive measureincludes applying a computer data model to the performance measures.

In related examples of embodiments, the computer data model is trainedbased on performance measures of individuals with known cognitiveconditions.

In related examples of embodiments, the computer data model is trainedusing a technique selected from the group consisting of: machinelearning, pattern recognition, regression analysis, and Monte Carlotechnique.

In related examples of embodiments, computing the cognitive measureincludes computing a hit rate, false alarm rate, correct response rate,or miss rate.

In related examples of embodiments, computing the cognitive measureincludes applying a signal detection technique selected from the groupconsisting: sensitivity index, receiver operating characteristics (ROC),and bias.

In related examples of embodiments, the cognitive measure is a compositemeasure computed using performance measures of the first plurality ofresponses to the first task and performance measures of the secondplurality responses to the second task.

In related examples of embodiments, the cognitive measure is a compositemeasure computed using at least two types of performance measures of oneof the first plurality of responses and the second plurality ofresponses.

In related examples of embodiments, the cognitive measure is a compositemeasure computed using non-performance information and performancemeasures of one or both of the first plurality of responses and thesecond plurality of responses.

In related examples of embodiments, non-performance information isselected from the group consisting of: demographic, age, gender, andhealth data of the individual.

In related examples of embodiments, the cognitive assessment provides adiagnosis of cognitive disorder.

In related examples of embodiments, the cognitive assessment is used tomonitor the individual's cognitive ability over time.

In related examples of embodiments, the cognitive assessment is used tomonitor an effect of therapy on the individual's cognitive ability.

In related examples of embodiments, the second stimuli are presented atexactly the same time with at least some of the first stimuli.

In related examples of embodiments, one of the second stimuli ispresented with an associated first stimuli consecutively with a slighttime differential.

The exemplary embodiments of computer-implemented methods describedabove may be implemented using a computer-implemented system comprisinga one or more processors; and a memory comprising instructions whichwhen executed cause the one or more processors to execute one or moresteps described above. In one embodiment, the exemplary embodiments ofcomputer-implemented methods described above may be implemented using acomputer-implemented system comprising a hand-held computing devicehaving one or more processors, a display component, an input device, anda sensor; and a memory comprising instructions which when executed causethe one or more processors to execute one or more steps described above.

The exemplary embodiments of computer-implemented methods describedabove may also be implemented as instructions encoded on anon-transitory computer-readable medium, the instructions beingconfigured to cause a computer processing system to execute one or moresteps described above. In one embodiment, the exemplary embodiments ofcomputer-implemented methods described above may be implemented asinstructions encoded on a non-transitory computer-readable medium, theinstructions being configured to cause a hand-held computing devicehaving a display component, an input device, and a sensor to execute oneor more steps described above.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying appendices and/or drawings illustrate variousnon-limiting, example, inventive aspects in accordance with the presentdisclosure:

FIG. 1 is a flow diagram of an exemplary embodiment of the cognitiveassessment tool.

FIG. 2 illustrates examples of computer processing systems on which thecognitive assessment tool may operate.

FIG. 3 depicts screen shots of an exemplary preferred embodiment of thecognitive assessment tool.

FIG. 4 depicts results of a pilot study of the exemplary preferredembodiment of the cognitive assessment tool.

FIG. 5 depicts an exemplary computer processing system for use inimplementing an exemplary cognitive assessment tool.

FIG. 6 depicts an exemplary computer processing system for use inimplementing an exemplary cognitive assessment tool.

DETAILED DESCRIPTION

The illustrated embodiments are now described more fully with referenceto the accompanying drawings wherein like reference numerals identifysimilar structural/functional features. The illustrated embodiments arenot limited in any way to what is illustrated as the illustratedembodiments described below are merely exemplary, which can be embodiedin various forms, as appreciated by one skilled in the art. Therefore,it is to be understood that any structural and functional detailsdisclosed herein are not to be interpreted as limiting, but merely as abasis for the claims and as a representation for teaching one skilled inthe art to variously employ the discussed embodiments. Furthermore, theterms and phrases used herein are not intended to be limiting but ratherto provide an understandable description of the illustrated embodiments.

Where a range of values is provided, it is understood that eachintervening value, to the tenth of the unit of the lower limit unlessthe context clearly dictates otherwise, between the upper and lowerlimit of that range and any other stated or intervening value in thatstated range is encompassed within the illustrated embodiments. Theupper and lower limits of these smaller ranges may independently beincluded in the smaller ranges is also encompassed within theillustrated embodiments, subject to any specifically excluded limit inthe stated range. Where the stated range includes one or both of thelimits, ranges excluding either both of those included limits are alsoincluded in the illustrated embodiments.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Although any methods andmaterials similar or equivalent to those described herein can also beused in the practice or testing of the illustrated embodiments,exemplary methods and materials are now described. All publicationsmentioned herein are incorporated herein by reference to disclose anddescribe the methods and/or materials in connection with which thepublications are cited.

When describing the methods and compositions of the present disclosure,the following terms include the following meanings unless otherwiseindicated, but the terms are not to be understood to be limited to theiraccompanying meanings as rather it is to be understood to encompass anymeaning in accordance with the teachings and disclosure of the presentinvention.

The term “cognitive measure” or “measure of cognitive ability orfunction,” as used herein, may refer to a representation of the state ofthe user's mental processes of perception, memory, judgment, reasoning,and/or the like. In some embodiments, the representation can be for aspecific type of function (e.g. memory). In some embodiments, therepresentation can be for several types of functions (e.g., memory andperception). In some embodiments, the representation can pertain to allof them as a whole.

The term “task,” as used herein, may refer to any method or process ofan individual responding to stimuli. In some embodiments, stimuli may bepresented specifically to measure cognitive function, making it an“active task.” In some embodiments, stimuli may be presented as part ofroutine computer device use and not specifically for cognitive function,making it a “passive task.”

The term “simultaneous,” as used herein, may refer to two or more thingsbeing in substantially the same time period (e.g., having no differencein time or a slight differential such as 0.1 second, 0.5 second, or 1second). For example, in certain embodiments, two or more things aresimultaneous if they both occur at the exact same time. In someembodiments, two or more things are simultaneous if they occurconsecutively separated by a slight time differential. In someembodiments, two or more things are simultaneous if they occur on arotating basis each for a short time period with no breaks in between.In some embodiments, two or more things are simultaneous if they are setfor the same period of time.

The term “multi-tasking,” as used herein, may refer to a user performingat least two tasks simultaneously. The tasks may be active or passivetasks.

The term “single-tasking,” as used herein, may refer to a userperforming only one task for a set period of time. The task may be anactive or a passive task.

The term “game-level,” as used herein, may refer to the discretestimulus magnitude values associated with a specific task in a videogame. Each level may correspond to a specific increment in a parameterrelated to task. Increasing levels may present increasingly difficulttasks.

The term “threshold,” as used herein, may refer to the level of stimulimagnitude of a task that is the limit of a person to perform the task toa specified level of correctness based on one or more predeterminedcriteria.

The term “stimuli,” as used herein, may refer to computer devicepresenting sensory events for the user that evoke a specific functionalreaction. For example, a reaction may be an interaction with thecomputer device. In some embodiments, stimuli may include a navigationpath through which the user is instructed to navigate. In someembodiments, stimuli may include interferences that distract the userfrom another task and evoke user response. In some embodiments, stimulimay include distracters that distract the user from another task andrequire no response from the user. In some embodiments, stimuli mayinclude multiple types of stimuli with different response requirements.

The term “distractor stimuli,” as used herein, may refer to a specificstimuli for a perceptual reaction task in which the user is not supposedto react to the stimuli or provide computer inputs. Providing inputs fora distractor stimuli is considered an incorrect response to the taskwhich presents the stimuli. In some embodiments, non-responses may beconsidered a response to such distractor stimuli (e.g., a correctresponse to a distractor stimuli may be the absence of response within atime window).

The term “neurotypical,” as used herein, may refer to a description of aperson who has no known cognitive deficits.

It must be noted that as used herein and in the appended claims, thesingular forms “a”, “an,” and “the” include plural referents unless thecontext clearly dictates otherwise. Thus, for example, reference to “astimulus” includes a plurality of such stimuli and reference to “thesignal” includes reference to one or more signals and equivalentsthereof known to those skilled in the art, and so forth.

It is to be appreciated the illustrated embodiments discussed below arepreferably a software algorithm, program or code residing on computeruseable medium having control logic for enabling execution on a machinehaving a computer processor. The machine typically includes memorystorage configured to provide output from execution of the computeralgorithm or program.

As used herein, the term “software” is meant to be synonymous with anycode or program that can be in a processor of a host computer,regardless of whether the implementation is in hardware, firmware or asa software computer product available on a disc, a memory storagedevice, or for download from a remote machine. The embodiments describedherein include such software to implement the equations, relationshipsand algorithms described above. One skilled in the art will appreciatefurther features and advantages of the illustrated embodiments based onthe above-described embodiments. Accordingly, the illustratedembodiments are not to be limited by what has been particularly shownand described, except as indicated by the appended claims. Allpublications and references cited herein are expressly incorporatedherein by reference in their entirety.

FIG. 1 is a general flow diagram of an embodiment of the cognitiveassessment tool as described herein. In one embodiment, the cognitiveassessment tool may be implemented on a computer device with user inputfeatures 101. The computer device may simultaneously present stimuli oftwo tasks 102, 103 to the user. Tasks 102 and/or 103 may include taskthe user engages in voluntarily for purposes other than cognitiveassessment or task assigned to the user 104 by a program for purposes ofcognitive assessment. In a preferred embodiment, task 102 is avisuomotor task and task 103 is a perpetual reaction task. The user maythen respond to both tasks 104 and those responses are detected ormeasured 105, 106 by the computer device 101 (e.g., the responses may bedetected as mouse clicks, screen taps, accelerometer readings, etc.).The computer device 101 may analyze the user responses to the tasks 105,106 and convert them into a cognitive assessment 107 that isrepresentative of the user's cognitive ability or function. In someembodiments, the cognitive assessment 107 may be based on a performancemeasure of responses to one specific task (e.g., performance measure oftask 1 110 or performance measure of task 2 111). In other embodiments,the cognitive assessment 107 may be a composite measures 112 based onperformances measures of responses to one or both tasks (e.g.,performance measures of task 1 110 and/or performance measures of task 2111). In some embodiments, cognitive assessment 108 may be based only onmeasures 110, 111, and 112 of user inputs while multi-tasking. In someembodiments, cognitive assessment 107 may be based on composite measure113 computed using additional external or non-performance information109, such as the user's demographic information or normative data.

FIG. 2 illustrates two types of computer processing systems 200 and 201with which embodiments of the present disclosure may be practiced. Inone embodiment, the computer system 200 may contain a computer 202,having a CPU, memory, hard disk and CD ROM drive (not shown), attachedto a monitor 203. The monitor 203 provides visual prompting and feedbackto the subject during execution of the computer program. Attached to thecomputer 202 are a keyboard 204, speakers 205, a joystick 206, a mouse207, and headphones 208. In some embodiments, the speakers 205 and theheadphones 208 may provide auditory prompting, stimuli, and feedback tothe subject during execution of the computer program. The joystick 206and mouse 207 allow the subject to navigate through the computerprogram, and to select particular responses after visual or auditoryprompting by the computer program. The keyboard 204 allows the subjector an instructor to enter alphanumeric information about the subjectinto the computer 202. In alternative embodiments, the computer mayincorporate additional input or output elements such as sensors tomonitor physical state or the user or video camera technologies tomonitor movement. The methods disclosed can be deployed on a number ofdifferent computer platforms e.g. IBM or Macintosh or other similar orcompatible computer systems, gaming consults, or laptops.

FIG. 2 also illustrates a suitable mobile computing environment, forexample, a tablet personal computer or a mobile telephone or smartphone201 on which embodiments of the cognitive assessment tool may bedeployed. In one embodiment, mobile computing device may be a handheldcomputer having both input elements and output elements. Input elementsmay include touch screen display 209, input buttons (not shown) thatallow the user to enter information into the mobile computing device,and internal sensors, such as accelerometer and gyroscope measurementunits (not shown), that allow the user to record movement of the device.The screen display 209 may provide visual prompting, stimuli, andfeedback to the user during execution of the computer program. Theoutput elements comprise the inbuilt speaker (not shown) that in someembodiments may provide auditory prompting, stimuli, and feedback to theuser during execution of the computer program. In alternativeembodiments, the mobile computing device may incorporate additionalinput or output elements such as a physical keypad to enter alphanumericinformation, attachments with sensors to monitor physical state, or aheadphone jack (not shown). Additionally, the mobile computing devicemay incorporate a vibration module (not shown) which causes mobilecomputing device to vibrate to provide stimulus or feedback to a userduring execution of the computer program.

FIG. 3 are screen shots of a preferred embodiment of the disclosedmethods, Project: EVO. Screen shot 300 shows an image of a target beingpresented for the perceptual reaction “Tapping” task. Screen shot 301shows a target that the user has reacted too. The computer collectsinformation on the user response. Screen shot 302 shows a usernavigating a path while attempting to avoid obstacles in the path, suchas the icebergs shown on the lower left portion of the screen. This isthe visuomotor “Navigation” task for the Project: EVO cognitiveassessment. The data from this task is also collected and analyzed.Screen shot 303 shows the user multi-tasking: responding to a targetwhile also navigating down a path.

FIG. 4 contains the results of a pilot study of Project: EVO assessment.This study compared the performance of young adults to older adultswhile multi-tasking. Both the mean of the reaction time (A) and thestandard deviation of reaction time (B) were significantly differentbetween the younger adults and older adults. These performance measureswere taken while the participants were engaged on a multitask thatincluded (1) a perceptual reaction task in which the user was made toperform a two-feature reaction task including target stimuli anddistractor stimuli by tapping on the screen or refraining from tapping,respectively, and (2) a visuomotor tracking task using the iPadaccelerometer to steer an avatar through obstacles down a graphicalcourse.

Details of various aspects of the cognitive assessment tool aredescribed below.

Multi-Tasking

Multi-tasking refers to a situation where a person is performing two ormore tasks simultaneously. It also denotes a situation where a person israpidly switching to and from different tasks or is performing multiple,different, short tasks in a row. Multi-tasking is a unique processbecause it requires the executive function controls that 1) decide toperform one action instead of another and 2) activate the rules of thecurrent task. Because multi-tasking has become increasingly a commonoccurrence, researchers have attempted to understand the mentalprocesses underlying multi-tasking and the relationship betweenmulti-tasking and academic achievement, learning, and memory (Charronand Koechlin, “Divided Representation of Concurrent Goals in the HumanFrontal Lobes” Science, 328: 360-363; Mayer and Moreno, 2003 “Nine waysto reduce cognitive load in multimedia learning” EducationalPsychologist, 38(2): 43-52; Junco and Cotton, 2010 “Perceived academiceffects of instant messaging use” Computers & Education, 56(2):370-378). In these settings, the main feature being investigated is thedecay in performance in the multi-task scenario relative to the scenariowhere individuals are only performing one single-task component of themulti-tasks.

Additionally, recent purpose-built cognitive paradigms have beenconstructed to study this phenomenon by measuring the difference betweensingle-task performance and performance in the same task whilemulti-tasking. The resulting multi-tasking cost is used for cognitivediagnostic (Int. Pat. No. WO2012/064999A1 by Gazzaley, A.).

Uniquely, our research finds that, contrary to what was previouslyassumed, performance data collected in the multi-tasking environment,other than the previously useful multi-tasking cost data, is alsoinformative of a user's cognitive state, and in some cases can be a moreinformative method than traditional cognitive assessments andtraditional multi-tasking “cost” measurements.

Computer Device

Performance of the many tasks that are accomplished on a computer can bemeasured with incredible accuracy, often surpassing the ability of thehuman to measure, store, and analyze the inputs of a user. The cognitiveassessment tool described herein may be implemented on a computerprocessing system with an input component. The computer processingsystem is suitable because it allows for the presentation of two tasksand measurement of user responses to the two tasks at the same time,something humans are not capable of doing with fidelity and reliability.The computer processing system also allows for the adaption ofdifficulty of both tasks independently. Additionally, without thetemporal resolution that the computer processing system is able toprovide, the performance measurements would not be effective cognitivemeasures. For example, the computer device can measure differences inthe inputs, such as the millisecond timing of keystrokes on a keyboardor clicks on touch screen, that are imperceptible to a human trying tomeasure the same task. In one embodiment of our disclosed methods, thedifference in mean reaction time to a perceptual reaction task betweenyoung adults and old adults is around one tenth of a second.

Computer devices have become integrated into many people's daily lives.They are now used for many types of communication, processing of data,and for entertainment purposes such as electronic video games. Whereasthis is not crucial for other cognitive tests, the ubiquitous presenceof computer devices and multi-tasking allow for passive measurements ofeveryday computer use as part of a cognitive assessment.

In one embodiment, the present disclosure provides computer-implementedmethods for measuring cognitive function of an individual, wherein themethod is implemented using a computer device having an input component.In some embodiments, the computer device is selected from the groupconsisting of a desktop computer, a laptop computer, a computer tabletdevice, a smart phone device, and a video game device. In someembodiments, the computer input device is selected from the groupconsisting of a mouse component, a stylus computer, a keyboardcomponent, a microphone, a sensor of physical state of the user (e.g.,accelerometer and/or gyroscope), and a touch screen display. It isappreciated that many such computer input methods are available, andadvances in computing technology will continue to provide new types ofinputs. The method of the current patent is dependent on an inputmodality, but importantly is independent of a specific type of inputmodality so long as the ability to reliably measure the input ismaintained, and thus the disclosed methods are applicable to current andfuture input modes.

Tasks

In one embodiment, a task includes stimuli presented on a computerdevice, evoking responses from the user. The stimuli that evokeresponses from the user may come in multiple forms. The stimuli may bechosen from a variety of stimulus modalities known in the cognitive art,including but not limited to visual, auditory, tactile, language basedor symbolic. The user response may also come in many different forms.The user response can also be chosen from a variety of modalities knownin the art, including but not limited to binary input (yes/no ortrue/false), choosing one or more options among many, constant input(continuously adjusting to changing stimuli e.g. steering a car down aroad), language based (typing or speaking a response), elements ofbiofeedback measured by a sensor connected to the computer-device (EEGsignals, accelerometer readings, etc.), and the like.

In one embodiment, a user is considered engaged in multi-tasking if theyare attending to and performing at least two tasks simultaneously undera few conditions. First, for example, the tasks may be considered to besimultaneous if the user is providing inputs to the two tasks as thesame time. For instance, a user could be providing movement inputs for amotor task through a joystick and at the same time providing inputs to areaction task with a mouse. Second, for example, the tasks may beconsidered to be simultaneous if the user switches between the two taskswithin a set amount of time. The set amount of time for switching couldbe considered about 1 tenth of a second, 1 second, about 5 seconds,about 10 seconds, about 30 seconds, about 1 minute, or 2 or moreminutes. The tasks can be presented to the user in any order known towork by one skilled in the art. For example, tasks may be presented in arotating order (e.g., A, B, C, . . . n, A, B, C . . . n, etc.); in apredetermined order set by someone familiar for a particular purpose(e.g. A, B, A, B, C, A, B, C, D, etc. or A, A, A, B, B, C); in a randomorder; or in a random order with some conditions on the distribution ofcertain tasks. For instance, a user could be providing language basedinputs to an email task for 1 minutes and responding to instant messageswith language based inputs for 30 seconds before returning to the emailtask for 2 minutes. Third, for example, the tasks may be considered tobe simultaneous if the tasks are completed within a short time periodand are done right after one another with no break. Short time period isconsidered about 1 tenth of a second, about 1 second, about 5 seconds,about 10 seconds, about 30 seconds, about 1 minute, or 1-2 minutes. Forinstance, a user may engage in web browsing for 30 seconds, followed byinstant messaging for 30 seconds, followed by game play for 30 seconds.Fourth, for example, the tasks may also be considered simultaneous ifthe user is instructed to complete at least two tasks within a setperiod of time. For two tasks, that period of time could be about 10seconds, about 30 seconds, about 1 minute, about 4 minutes, about 5minutes, about 7 minutes, or 10 minutes or more.

The tasks in which the user is engaged may have levels of difficulty. Insome embodiments, at least one task may have a constant level ofdifficulty. In some embodiments, at least one task has a variable levelof difficulty. When difficulty can be varied for a task, it can bevaried based on a schedule that does not depend on user inputs or it canbe varied based on the inputs of the user which is referred to herein asan “adaptive task.” In one embodiment, the adaptive task increases indifficulty when the user gives a correct response and decreases indifficulty when a user gives an incorrect response. Though the method ofincreasing a difficulty of a task is dependent on the specific task,generally the difficulty of a task may be increased by increasing thenumber of features a user must attend to, decreasing the perceptualsalience, increasing the frequency of required responses from a user,among other ways known to one skilled in the art.

For the purposes of this description, tasks performed on a computerdevice may be divided into two categories. The first category of tasksare those in which a user is asked to respond a certain way to aspecific stimuli for the purposes of cognitive assessment, and/or thetasks are purposefully structured to serve as an assessment (hereinafterreferred to as “active tasks”). The second category of tasks are thosein which a user is voluntarily responding to stimuli for purposes otherthan cognitive measurement, and/or are not structured to be a reliablemeasurement modality (hereinafter referred to as “passive tasks”). Theterm “task” in this disclosure encompasses both active and passive tasksunless otherwise specified.

There are numerous passive tasks that can be monitored by the computerdevice without being obtrusive to the user. Suitable passive tasksinclude but are not limited to responding to written communicationthrough a keyboard, web browsing with mouse clicks, web browsing throughthe keyboard, reading and progressing to new content through mouseclicks or touch screen taps, playing games with inputs described above,editing photos, and any other tasks that involve using one's smartphoneor tablet or other mobile device and have tactile, auditory, or motioninput, and other tasks in the same vein. The length of the passive taskcan be considered the entire time a user is engaged in the task at onetime or a pre-determined amount of time ranging from 30 seconds or less,about 1 minute, about 4 minutes, about 7 minutes, about 10 minutes, to15 minutes or more.

In some embodiments, the user performing at least two taskssimultaneously involves the user performing at least one passive task.In some embodiments, the user performing at least two taskssimultaneously involves the user performing at least two passive taskssimultaneously. One embodiment of a user performing at least two passivetasks simultaneously is a user writing an email and also responding toinstant messaging questions from a co-worker. Another preferredembodiment of a user performing two passive tasks simultaneously is auser reading a web page and also monitoring a twitter feed.

In a preferred embodiment of the disclosed methods, a user performing atleast two passive tasks simultaneously involves a user performing atleast two passive tasks simultaneously in a video game. Computerizedvideo games often present situations in which a user must perform morethan one task. For example, a user can be walking an avatar around agame environment while simultaneously changing weapons. It has alreadybeen shown that casual video games overall performance correlates tospecific cognitive functions (Baniqued, Lee, Voss, et al., “Sellingpoints: What cognitive abilities are tapped by casual video games?” ActaPsychol (Amst.) 2013; 142(1):74-), but the cognitive assessment tooldescribed herein improve upon that state of the art because it is basedon situations in which a user is multi-tasking in the gaming environmentand it takes specific user inputs from game play as performance measuresinstead of just overall score on the game.

There are also many ways to provide active tasks for a user. Forexample, there are multiple tasks that evaluate a user's cognitiveabilities in the following domains: attention, memory, motor, reaction,executive function, decision-making, problem-solving, languageprocessing, and comprehension, among others. The active task can last aslong as the user is willing to engage in the task or for a prescribedamount of time, 30 seconds or less, about 1 minutes, about 4 minutes,about 7 minutes, about 10 minutes, and 15 minutes or more.

In some embodiments, a user performing at least two tasks simultaneouslyinvolves a user performing at least one active task. In someembodiments, a user performing at least two tasks simultaneouslyinvolves a user performing at least two active tasks simultaneously. Thetwo tasks performed simultaneously can be assessing the same cognitivedomain listed above or assessing different cognitive domains. Apreferred embodiment of a user performing at least two active taskssimultaneously is a user performing a visuomotor task and a perceptualreaction task simultaneously. In one embodiment, performing a visuomotortask involves a presentation of visual stimuli that require fine motormovement as reaction to the stimuli. In some embodiments, the visumotortask is a continuous visuomotor task, altering the visual stimuli andrecording the motor movements of the user at, e.g., 1, 5, 10, or 30times per second. One embodiment of stimuli for a visuomotor taskrequiring fine motor movement may be a visual presentation of a paththat an avatar must stay on. This path may have obstacles that the useris instructed to avoid and/or specific locations that the user ininstructed to cross. In such an embodiment the fine motor reaction couldbe, among other things, tilting a device with an accelerometer to steerthe avatar to keep it on the path, while avoiding the obstacles, andcrossing the desired locations. In one embodiment, presenting aperceptual reaction task involves presenting both distractor stimulithat do not require a response from the user and target stimuli thatrequire a response from a user. In one embodiment, the distractorstimuli and the target stimuli are differentiated by shape. In anotherembodiment, the distractor stimuli and the target stimuli aredifferentiated by color. In another embodiment, the distractor stimuliand the target stimuli are differentiated by shape and color, forexample a user has to respond to red circles but not green circles orred squares. In some embodiments, a user performing at least two taskssimultaneously involves a user performing at least three active taskssimultaneously. A preferred embodiment of a user performing at leastthree active tasks simultaneously is a user performing a visuomotortask, a perceptual reaction task, and a memory tasks simultaneously.

In some embodiments, the tasks the user is performing are adaptivetasks. The tasks can be adapted or modified in difficulty by any methodsknown by one of ordinary skill in the art, such as staircase proceduresand maximum likelihood procedures. Such difficulty adaption may be usedto determine the ability of the participant. In a preferred embodiment,the difficulty of the task adapts with every stimuli that is presented,which could occur more often than once every 10 seconds. In analternative embodiment, the difficulty of a continuous task adapts on aset schedule, such as, e.g., every 30 seconds, 10 seconds, 1 second, 2times per second, or 30 times per second.

In some embodiments, a video game is used to provide an assessmentmedium in which a user is asked to perform at least two active taskssimultaneously. One advantage of presenting these specific tasks in avideo game is that it allows features that can encourage the participantto perform at the highest levels possible, such as by providing rewardsand creating an engaging interface. In a preferred embodiment, a userperforming at least two active tasks simultaneously in a video gameinvolves a user performing a visuomotor task and a perceptual reactiontask simultaneously. In one embodiment, presenting a perceptual reactiontask involves presenting both distractor stimuli that do not require aresponse from the user and target stimuli that require a response from auser. In one embodiment, the distractor stimuli and the target stimuliare differentiated by shape. In another embodiment, the distractorstimuli and the target stimuli are differentiated by color. In anotherembodiment, the distractor stimuli and the target stimuli aredifferentiated by shape and color, for example a user has to respond tored circles but not green circles or red squares.

User Inputs

The user may respond to tasks by interacting with the computer device.In one embodiment, the cognitive assessment tool obtains user responsethrough an input modality, but importantly the specific type of inputmodality can vary so long as the ability to reliably measure the inputis maintained, and thus the described methods are applicable to currentand future input modes. Examples of inputs for a desktop computerinclude a keyboard for alpha-numeric or directional inputs; a mouse forgo/no go clicking, screen location inputs, and movement inputs; ajoystick for movement inputs, screen location inputs, and clickinginputs; a microphone for audio inputs; and a camera for still or motionoptical inputs; sensors such as accelerometer and gyroscopes for devicemovement inputs; among others. Example inputs for a video game systeminclude but are not limited to a video game controller for navigationand clicking inputs, a video game controller with accelerometer andgyroscope inputs, and a camera for motion optical inputs. Example inputsfor a mobile device or tablet include a touch screen for screen locationinformation inputs, virtual keyboard alpha-numeric inputs, go/no gotapping inputs, and touch screen movement inputs; accelerometer andgyroscope motion inputs; a microphone for audio inputs; and a camera forstill or motion optical inputs, among others. Additionally, thesedevices can integrate physiological sensors to incorporate inputs fromthe user's physical state. The method of integrating physiologicalsensors as inputs is dependent on having a physiological input, butimportantly is independent of the specific type of input modality, andthus the described methods are applicable to current and futurephysiological input mode. Examples of physiological measurements for thedisclosed methods include but are not limited to electroencephalogram(EEG), magnetoencephalography (MEG), heart rate, heart rate variability,blood pressure, weight, eye movements, pupil dilation, electrodermalresponses such as the galvanic skin response, blood glucose level,respiratory rate, and blood oxygenation.

Measurements

It is known to one skilled in the art that multi-tasking tests areuseful in that they allow one to measure the difference in performanceof a task when multi-tasking and when single-tasking (multi-taskingcost) as a cognitive measure (Int. Pat. No. WO2012/064999A1 by Gazzaley,A.). However, the inventors have unexpectedly found that other measuresof performance during multi-tasking are as useful as, or in some casesmore useful than, multi-tasking cost. The multi-tasking performancemeasures may be considered to be fundamentally different measures ofcognitive function than multi-task cost measures and traditionalsingle-task cognitive measures known to one skilled in the art. Thefollowing measures described are all ones taken while a user is in amulti-task environment, unless it is explicitly mentioned that it is notmeasured while multi-tasking. It is appreciated that any of a variety ofcognitive performance measurements usually used for single-task may beuseful in the disclosed methods. Suitable measurements can be made onboth adaptive and non-adaptive tasks, as described in the task sectionabove, as the case may be.

Performance measures may be dependent on the specific task presented andthe category of cognitive function that is being examined. As previouslystated, one embodiment can have tasks associated with one or more of thefollowing cognitive domains: attention, memory, motor, reaction,executive function, decision-making, problem-solving, languageprocessing, and comprehension, among others. In these domains,performance measures of user inputs or responses may be used to createthe measure indicative of cognitive function. For example, performancemeasures may include response time, task completion time, number oftasks completed in a set amount of time, preparation time for task,accuracy of responses, accuracy of responses under set conditions (e.g.,stimulus difficulty or magnitude level and association of multiplestimuli), number of responses a participant can register in a set timelimit, number of responses a participant can make with no time limit,number of attempts at a task needed to complete a task, movementstability, accelerometer and gyroscope data, self-rating, among othersknown in the art.

In one embodiment, the performance measure may be reaction time. In apreferred embodiment, reaction time is measured as the reaction time toa perceptual reaction task. Further, if the perceptual reaction tasksincludes stimuli that are distractors, those that the participant shouldnot respond to, the reaction time can be measured as either the reactiontime to any response to any stimuli, reaction time only to responses tothe correct non-distractor stimuli (target stimuli), or reaction time todistractor stimuli—also known to one skilled in the art as the “falsealarm” reaction time.

In some embodiments, the performance measure may be correctness ofresponses, such as the quantity of correct responses over a set numberof stimuli. In a preferred embodiment, the correct responses may bemeasured as the correct responses to a perception reaction task. For aperception reaction task with distracters (i.e., stimuli the user shouldnot respond to), correct reactions may be calculated as the number oftimes a user responds to the target stimuli, or as the number ofresponses to the target stimuli added to the number of non-responses tothe distractor stimuli. In some embodiments, the performance measure maybe the quantity of incorrect responses to a task over a set number ofstimuli. In a preferred embodiment, the incorrect responses may bemeasured as the incorrect responses to a perception reaction task. For aperception reaction task with distracters (i.e., stimuli the user shouldnot respond to), incorrect reactions may be calculated as the number oftimes a user responds to the distractor stimuli, or as the number ofresponses to the distractor stimuli added to the number of non-responsesto the target stimuli.

In some embodiments, the performance measure may be the stimulimagnitude at which the user is able perform a task correctly orincorrectly in an adaptive task. In a preferred embodiment, the stimulimagnitude may be the speed down a path for a visuomotor “navigating”task. In another preferred embodiment, the stimuli magnitude may be thereaction window time given to respond to a perceptual reaction task.

From the performance measures calculated from user inputs, furtheranalysis can be completed to create more complex measures of cognitivefunction. In some embodiments the measure indicative of cognitivefunction reported is a complex cognitive measure. There are severalmethods of creating complex cognitive measures, e.g.: using standardstatistical summary methods, applying signal detection theory, applyingpsychophysics performance metrics, combining data to create a compositemeasures, and examining measures over time.

In some embodiments the complex cognitive measure may be a statisticalsummary measure. Summary statistics employed by one skilled in the artinclude: mean, variance through standard deviation or standard error,running average, time spent in a certain performance level, being aboveor below a specified value, percent, correlation, Root Mean Square Error(RMSE), R2 correlation coefficient, confidence intervals, fit tostandard statistical distributions such as T-score or Z-score, summaryaccording to a normative data set, Bayesian statistical methods,measurements created from a Principle Component Analysis, measurementscreated from machine learning, specifically pattern recognition betweengroups, and parameters from applying statistical model such asregression coefficients and Monte Carlo simulation parameters, and thelike.

In one embodiment, the statistical summary measurement recorded may bethe mean performance at a task over a period of time. In a preferredembodiment, the mean performance at a task consists of the meanperformance when the task continuously adapts difficulty to the user'sprevious performance on the task. A period of time can be chosen as theamount of time a person performs a task at one time or can be apredetermined amount of time such as about 30 seconds, about 1 minute,about 4 minutes, about 10 minutes, or more than 10 minutes. In apreferred embodiment, the mean performance game-level is measured as themean reaction time window for a perceptual reaction task when thatwindow is increased when the user responds incorrectly and decreaseswhen a user responds correctly. Further, reaction time windows can belabeled as “levels” in a game, with the level number increasing as thereaction time window decreases. In some embodiments, the meanperformance at a task can be measured as the mean performancegame-level. In some embodiments, the mean performance game-level is themean performance game-level of a perceptual reaction task. In anotherpreferred embodiment, the mean performance level is the mean stimulimagnitude of an adaptive visuomotor task. Further, for a “navigating”visuomotor task, navigation speed and number of obstacles can be used todetermine a navigation game level, with the level increasing withincrease speed and/or increasing number or size of obstacles. Thisnavigation game level can be used to calculate the mean performancelevel.

In another embodiment, the statistical summary measurement may be thestandard deviation of performance level at a task over a period of time.In a preferred embodiment, the standard deviation of performance levelat a task is the standard deviation of performance level when the taskcontinuously adapts difficulty to the user's previous performance on thetask. A period of time can be chosen as the amount of time a personperforms a task at one time or can be a predetermined amount of timesuch as about 30 seconds, about 1 minute, about 4 minutes, about 7minutes, about 10 minutes, or more than 10 minutes. In a preferredembodiment, the standard deviation of performance level is measured asthe standard deviation of the reaction time window for a perceptualreaction task when that window is increased when the user respondsincorrectly and decreases when a user responds correctly. Further,reaction time windows can be labeled as “levels” in a game, with thelevel number increasing as the reaction time window decreases. Thisgame-level can be used to calculate the task's standard deviation ofperformance level in addition to the actual reaction time window. Inanother preferred embodiment, the standard deviation of performancelevel is the mean stimuli magnitude of a visuomotor task. Further, for a“navigating” visuomotor task, navigation speed, shape of the course, andnumber of obstacles can be used to determine a navigation game level,with the level increasing with increase speed, increasing frequency ofturns, decreasing turning radius, and/or increasing number or size ofobstacles. This navigation game-level can be used to calculate thestandard deviation of performance level.

In one embodiment, the statistical summary measurement may be meanreaction time over a period of time. A period of time can be chosen asthe amount of time a person performs a task at one time or can be apredetermined amount of time such as about 30 seconds, about 1 minute,about 4 minutes, about 7 minutes, about 10 minutes, or more than 10minutes. In a preferred embodiment, mean reaction time is measured asthe mean reaction time to a perceptual reaction task. Further, if theperceptual reaction tasks includes stimuli that are distractors, thosethat the participant should not respond too, the mean reaction time canbe measured as either the mean reaction time to any response to anystimuli, mean reaction time only to responses to the target stimuli, ormean reaction time to distractor stimuli—also known to one skilled inthe art as the “false alarm” reaction time.

In one embodiment, the statistical summary measurement taken may be thestandard deviation of a set of reaction times. These reaction times canbe compiled for analysis by choosing all reaction time events while aperson performs the task analyzed or in a set amount of time such asabout 30 seconds, about 1 minute, about 4 minutes, about 7 minutes,about 10 minutes, or more than 10 minutes. In a preferred embodiment,standard deviation of reaction time is measured as the standarddeviation of reaction time in a perceptual reaction task. Further, ifthe perceptual reaction tasks includes stimuli that areinterference/target stimuli and distractor stimuli the standarddeviation of reaction time can be measured as either the standarddeviation of reaction times to any response to any stimuli, reactiontimes only to responses to the target stimuli, or reaction times todistractor stimuli.

In one embodiment, the statistical summary measurement taken may be thecorrelation of performance level with the order in which a task isperformed. In one preferred embodiment, correlation of performance levelwith the order in which a task is performed is the correlation ofgame-level of a perceptual reaction tasks with the order of theperceptual reaction task. In one preferred embodiment, correlation ofperformance level with the order in which a task is performed is thecorrelation of navigation game-level of a visuomotor tasks with the timeengaged in the visuomotor task. In one preferred embodiment, correlationof performance level with the order in which a task is performed is thecorrelation of hit rate and false alarm rate (as described below undersignal detection theory) with the order of the perceptual reaction task.

In some embodiments, the statistical summary measurement taken may becreated from Bayesian statistical methods. For example, the Bayesiananalysis can include but is not limited to the probability of a correctresponse given an incorrect response and the probability of an incorrectresponse given a correct response.

In some embodiments, the statistical summary measurement taken may becreated via Principal Component Analysis or a similar technique totransform multiple direct performance measures into a smaller set ofindirect measures summarizing the most significant contributors tovariability within the measurements. With a Principal Component Analysismethod, the multi-tasking performance measures from multiple samples arecombined into one data set. For example, the set may consist ofperformance measures A, B, C, D, and E for a set of experimentparticipants (Participants #1-100). This data set may be the input to anorthogonal transformation that converts the performance measures A-Einto a set of linearly uncorrelated variables, named the principlecomponents. The outputs may be composed of eigenvectors of the originalvariable set. When using such a method, the outputs or the principlecomponents are themselves a metric of cognitive function. The PrincipleComponent Analysis is one way of creating composite variables.

In some embodiments, the statistical summary measurement taken may bederived from machine learning. In one embodiment, classificationtechniques may be used to train a computer data model using theperformance measures of a labeled population of subjects (e.g., subjectswith known cognitive disorders or abilities). The trained computer datamodel may be applied to a user's performance measures to predict whichpopulation label (e.g., cognitive disorder) should be assigned to theuser. For example, machine learning may be implemented by using clusteranalysis. Each observation of participating individuals (e.g., thecognitive assessment tool may be used to determine performance measuresof each individual) is categorized into subsets or clusters. In onecase, the subset or cluster labels may be the cognitive disorders eachparticipant in an experiment is diagnosed with. Using the clusteranalysis machine learning techniques, outputs may represent similaritymetric of each subset and the separation between different subsets. In adifferent example, a supervised machine learning may be based onartificial neural networks. In such a case, the performance measures ofparticipating individuals with known cognitive abilities may be used totrain the neural network algorithm to better understand the complexrelationships between the different performance measures. Once trained,the neural network may be applied to a user's performance measures tooutput a cognitive measure, which may represent a prediction of his/hercognitive abilities.

In another embodiment, regression or Monte Carlo techniques may be usedto generate computer data models to describe observed performancemeasures and predict certain user's cognitive abilities based on his/hercognitive performance. In some embodiments, the ability being predictedmay be outside of the assessment environment (e.g., gaming environment),such as external tests of attention or performance on standardizedacademic tests. For example, a model may be trained using the multi-taskperformance measures of a group of individuals and their externalmeasures of cognition (e.g., their known cognitive abilities, includingcognitive disorders, attention span, performance on standardized tests,etc.). Using Monte Carlo or regression techniques, a computer data modelmay be trained to predict the external measure of cognition of anindividual using that individual's multi-task performance measures. Inaddition to the multi-task performance measures, other potentiallypredictive variables may also be used, such as EEG and demographicmeasures.

In one embodiment, the statistical summary measurement taken may bebased on a summary of accelerometer data. Statistical summaries of theaccelerometer vector components (x, y, z), taken individually or as acomposite, may be used to measure performance. Statistical summaries canbe but are not limited to, e.g., the mean and standard deviation. Inaddition, accelerometer data can be compared to an ideal function fromwhich deviance from the ideal measures can be computed. In addition,accelerometer data can be treated as a waveform to measure the spectralproperties of the user's performance. An example of such an analysis mayinvolve a Fourier transform of the accelerometer data to produce gain,phase, and amplitude values representing the user's performance profileover the course of gameplay. In one embodiment, the accelerometer datamay be captured at, e.g., 30 times per second, so that the user's exactmovements of the mobile device are recorded. The raw accelerometer datawould indicate the amount of acceleration in the x, y, and z directionsat any moment in time. The accelerometer data, which has the form of afinite sequence of equally spaced samples can be put through a Fouriertransform, which outputs the information about the frequency domain, ora list of coefficients of a finite combination of sinusoids, ordered bytheir frequencies. The outputs may indicate the user's motor responsecapabilities, the degree of cognitive and motor demand placed on them bythe visuomotor task, and the timing of these demands with respect tocontemporaneous perceptual reaction task trials.

In some embodiments the complex cognitive measure may be computed usingsignal detection theory. Signal detection theory can be used by one ofordinary skill in the art to calculate a sensitivity index (d′ or A′),Bias, ROC, hit rate, false alarm rate, and the like from the userresponses and performance measures.

In a preferred embodiment, the metric from signal detection theoryrepresenting cognitive function may be the hit rate from a perceptualreaction task. In that context, hit rate may be defined as the number ofcorrect responses to a target stimuli divided by the total number oftarget stimuli presented. In another preferred embodiment, the metricfrom signal detection theory representing cognitive function may be thefalse alarm rate from a perceptual reaction task. In such context, thefalse alarm rate may be defined as the number of responses to adistractor stimuli divided by the number of distractor stimulipresented. In another preferred embodiment, the metric from signaldetection theory representing cognitive function may be the miss ratefor a perceptual reaction task. In such context, the miss rate may bethe number of non-responses to a target stimuli divided by the number ofincorrect responses, including the non-responses to a target stimuliadded to the number of responses to a distractor stimuli. In anotherpreferred embodiment, the metric from signal detection theoryrepresenting cognitive function may be the correct response rate,defined as the proportion of correct responses not containing signal.The correct response rate may be calculated as the number ofnon-responses to the distractor stimuli divided by the number ofnon-responses the distractor stimuli plus the number of responses to thetarget stimuli.

In some embodiments the complex cognitive measures may be created frompsychophysics methods of the user responses or performance measures.Psychophysics theory can be used by someone skilled in the art tomeasure a user's thresholds through the method of limits, method ofconstant stimuli, or method of adjustment, among many othermeasurements.

In one embodiment, the psychophysics metric determined from user inputsmay be based on performance threshold. This threshold may be defined asthe maximum stimulus magnitude (such as speed in a visuomotor navigationtask) of a task for which a user can achieve a specified ratio ofcorrect responses to incorrect responses in an adaptive task over time.For instance, the threshold may be defined as the maximum stimulusmagnitude of a task for which a user can correctly perform the taskabout 1%, about 10%, about 50% of the time, about 70% of the time, about80% of the time, or between 90-100% of the time. The threshold may alsobe defined as the maximum stimulus magnitude of a task for which a userachieves a specified ratio of correct responses to incorrect responseswhen the stimulus magnitude is increased incrementally. In addition, thethreshold may be characterized by the quantity or percent of stimulithat are responded to correctly above or below the threshold level in anadaptive task. In a preferred embodiment, the performance threshold maythe reaction time window at which the user can to continuously achieve80% correct responses to a perceptual reaction task. Further, reactiontime windows may be labeled as “levels” in a game, with the level numberincreasing as the reaction time window decreases. This game-level may beused to represent the task's performance threshold in addition to theactual reaction time window. In another preferred embodiment, theperformance threshold may be the stimulus magnitude (i.e., speed of theobject of the task) at which a user is able to perform a visuomotor taskat 80% correct. Further, for a “navigating” visuomotor task, navigationspeed and number of obstacles can be used to determine a navigationgame-level, with the level increasing with increase speed and/orincreasing number or size of obstacles. This navigation game-level maybe used to represent the navigation performance threshold. In anotherpreferred embodiment, the performance threshold may be the combinationof the maximum stimuli magnitudes at which the user performs avisuomotor task at 80% correctness and perceptual reaction task at 80%correctness. For instance, this measurement may be represented as themean of the game-levels previously described in this paragraph. Inanother preferred embodiment, the performance threshold may be thereaction time threshold.

In some embodiments the complex cognitive measure may be a compositemeasure. Examples of composite measures are combinations of two or moreperformance measures from one task, combinations of two or moreperformance measures from more than one task, and combinations ofperformance measures with external information.

In some embodiments, the composite measure may be a composite of atleast two measures from the same task. Composite measures may be createdin at least two ways. In the first way, the composite measure may be onethat is created prospectively to represent a known cognitive orpsychological construct. A list of such constructs follow. In apreferred embodiment, the composite of two measures from the same taskis the reaction time for a response to a stimuli divided by the reactiontime window in which a user can possibly respond to the reaction time inthe perceptual reaction task, which provides an indicator of how theuser allocated the time allotted to them to respond. In anotherpreferred embodiment, the composite of two measures from the same taskis the mean reaction time to stimuli divided by the standard deviationof the reaction time in a perceptual reaction task, which provides anormalized measure of reaction time variability that can be used tocompare across subjects with diverse baseline characteristics. Inanother preferred embodiment, the composite measure includes the meanreaction time added to or averaged with the standard deviation ofreaction time for all responses, which provides a way of balancing theimpact of baseline performance and variability. In another preferredembodiment, the composite of two measures from the same task is thecorrelation of the reaction time to stimuli magnitude or difficulty ofthe task. The correlation of the reaction time to the stimuli magnitudeof a task can be the correlation of the reaction time to and thegame-level of a perceptual reaction task when the game-level changesduring measurement. This is another indicator of how the user allocatesthe time allotted to them.

In some embodiments, the composite measures may be a composite of atleast two measures from at least two different tasks. In one embodiment,the composite measure of two measures from two different tasks may bethe difference in performance of the two tasks. In a preferredembodiment, the difference in performance may be the difference in thegame-level of a perceptual reaction task and the game-level of anavigation task. The statistical summary measurement of the measure maybe whether or not the difference in the performance game-level of thenavigation task and the performance game-level of the perceptualreaction task is greater than or less than the running average of thedifference. This measurement speaks to the degree to which the user isadjusting their strategy over time to allocate their resources betweenthe two tasks. In one embodiment, the composite measure of two measuresfrom at least two different tasks may be tradeoff summary. One way tocalculate the tradeoff summary may be by dividing the threshold for onetask by the threshold for another task. In a preferred embodiment, thetradeoff summary may be the game-level threshold for a perceptualreaction task divided by the game-level threshold for a visuomotor task.The tradeoff summary is another indicator of the user's allocation ofresource between tasks.

In some embodiments, the composite measure may be a composite ofmulti-task performance measures and external information about the user.External information, or information not obtained from the instantmulti-tasking assessment, that can be useful for determining cognitivemeasures include measurements from the same task under differentcircumstances, measurements from of a different cognitive task,performance on non-computerized tasks, non-performance information suchas demographic information about the user, symptom and diseaseinformation, geographic and other contextual information, and the like.In one embodiment, the composite of user inputs and external data may bethe composite of multi-tasking performance measures and differentperformance measures of user inputs while single-tasking. This compositevariable is distinguished from the prior interference or multi-task costmeasurements because these measures do not directly compare the sameperformance variable in the single-task and multi-task environment. Thistype is often a representation of the second way of creating compositecognitive measures, by using statistical methods or models to createunique variables not prospectively determined to evaluate a specificconstruct, though a construct may be determined afterwards. Examples ofmethods for creating such variables are Principal Component Analysis andneural networks machine learning. In a preferred embodiment, thecomposite of single-task and multi-task measurements may be theaggregate of 1) standard deviation of a perceptual reaction taskgame-level at which a the user correctly reacts to a non-distractorstimuli within the time window while single tasking, and 2) meanreaction time of the correct response to non-distractor stimuli whilemulti-tasking. In another preferred embodiment, the composite ofsingle-task and multi-task measures may be the quantity (standarddeviation of a game-level of a perceptual reaction task performed inisolation plus the mean reaction time of a perceptual reaction taskwhile multi-tasking minus the mean reaction time of a perceptualreaction task in isolation) divided by two.

In some embodiments, patterns of performance measures created from themulti-tasking assessment tool may be used for evaluation of cognitiveabilities. For example, pattern recognition may be based on performancemeasures of a set of neurotypical individuals and distinct groups withdifferent known medical diagnoses. These groups may be symptomaticallysimilar, such as having sensory processing disorder and autism, orhaving cerebrovascular dementia and Alzheimer's disease. By using, e.g.,machine learning or classification analysis to process the differenttypes of performance measures from the multi-tasking assessment tool andknown cognitive assessments, clinically similar groups may be able to bediagnosed or differentiated. For example, if a set of three predictivemeasures are used, such as reaction time variability, game level of acontinuous motor task, and false alarm rate of a reaction task, theAlzheimer's group may be differentiated by high false alarm rates andthe cerebrovascular group may be differentiated by both the lower gamelevel of a continuous motor task and the high false alarm rate.

In some embodiments, the complex cognitive measure may be a measuretaken over time. Measures taken over time include change in user inputsover time, progression in a task or game, and interaction variables witha task or game.

In one embodiment, the measure taken over time may be the change in userinputs over time. As is known in the art, the ability for one to acquireor maintain particular skills can change with cognitive function. In onepreferred embodiment this change in user inputs over time may becalculated as the change in any metric over a specific period of use,representing a change in the task performance. The specific period ofuse in these cases may be time based such as 10 minutes of taskengagement, 20 minutes task engagement, 30 minutes task engagement, or60 minutes of task engagement. In other cases, the specific period ofuse may be determined by the number of instances the task was engaged inby the user, such as the number of times a game was played (2 times, 4times, 7 times, 10 times, 25 times, 35 times, 50 times, 70 times, 100times, 140 times, or 150 more times) or the number of writtencommunications created. The specific period of use may also bedetermined by a set time period that does not account for the number ofinstances or amount of time a task is completed. For example, the changein user inputs can be calculated as the change over 1 hour, 1 day, 2days, 1 week, 2 weeks, 1 month, 3 months, 6 months, 1 year, or more than1 year. The reverse of this metric may also be useful, namely the amountof time or engagement it takes to achieve a specific change in ameasure.

In one embodiment, the change in a user input over time may be thechange in mean reaction time from the first time a user engaged in aperceptual reaction task to the seventh time a user engaged in the sametask while multi-tasking. In another preferred embodiment, the change inskill level may be measured as the length of time required to attain aspecified change in user inputs. For example, the change in user inputsmay be measured as the time it takes to reduce the mean reaction time by100 milliseconds in a perceptual reaction task or the time to achievethe next game level of threshold performance.

In one embodiment, at least two tasks may be performed by the user in avideo game environment and the complex cognitive measure of user inputsover time may be the ability of the user to progress through the game.One preferred embodiment of user progress through the game is themeasurement of the amount of time a user spends at levels close to orexceeding the users previously calculated threshold performance level ofboth tasks. This amount of time can be reported in summary methods knownto one skilled in the art such as the maximum time spent at the specificlevels and the total time spent over many measurements, among others.Another preferred embodiment of user progress through the game may bethe number of levels a user can achieve in a set period of time. Anotherpreferred embodiment of user progress through the game may be the numberof times a user fails to meet or improve the threshold levels ofperformance to move on to the next game level.

In another embodiment, the inputs from the user may be used to createcomplex cognitive measures over time that represent measures of behaviorand interaction with the cognitive assessment tool. One preferredembodiment of measuring interaction with the cognitive assessment toolis the measurement of compliance—whether the user interacts with adevice in the way in which he or she is instructed. For instance, if auser is instructed to be in a multi-tasking environment for a set periodof time or set number of task activities per day, the measure could bethe percent of days in a month that user meets the requirements. Anotherpreferred embodiment of the interaction measurement may be themeasurement of the frequency of being in the multi-tasking environment.Another preferred embodiment of the interaction measurement may be themeasurement of the patter of interaction with the device, for instanceif the user engages is multi-tasking once per day or multiple times perday. In another embodiment, the complex cognitive measure thatrepresents behavior may be the user's attention to irrelevant featuresof a video game. For example, if the stated goal of the game is toperform a visuomotor task and a perceptual reaction task in a videogame, but a third task such a coin collection is also included, howoften the user engages in the third task may be used to compute acognitive measure.

As known by one skilled in the art, the measures described in thissection may change when a user is giving maximum effort. Therefore,these measures may be taken over any time period or only when it can beidentified that a user is giving maximum effort and each would havedifferent cognitive meanings. In one embodiment, analysis of user inputsare isolated to the user inputs when the level of difficulty is nearthreshold levels for two or more adaptive tasks. In a preferredembodiment, the user inputs analyzed may be the user inputs for anadaptive visuomotor task and an adaptive perceptual reaction task whenthe game-levels of both tasks approach threshold levels.

It is appreciated that many of the passive tasks may not have standardcognitive measures that can be taken from the user input. In these casesthere may be a few intermediate analysis steps on the user inputs. Firstis to identify when the multi-tasking is occurring and processing thedata.

The identification of multi-tasking when passively monitoring a user cantake a few different forms: determining when a user is switchingfrequently between different programs on a computational device andidentifying when a user is engaged in a game that involvesmulti-tasking.

Processing the data from passive monitoring, as known to one skilled inthe art, may involve identifying false multi-signaling flags,identifying and in some cases removing outlier data points, and trackingpatients over a longer period of time to distinguish the signal from thenoise. Following this set, the same techniques for evaluating userinputs in active tasks described herein may be applied.

Use of Measurements

The cognitive measurements described in this disclosure may be useful inmany domains, including healthcare, employment evaluation, andeducation, among others.

In the medical setting, the cognitive assessment tool may be used bythemselves, or with other clinical measurements, to diagnose aparticular disease or medical condition. In another embodiment in themedical setting, the tool can be used to assess the severity of thecognitive deficit associated with a disease or medical condition.Particular populations for which such a cognitive measure would bebeneficial for diagnosis are listed, in part, in the following sectionof the disclosure.

In one embodiment, the cognitive assessment tool may be used to monitorcognitive deficits. Monitoring cognitive deficits allows patients,clinicians, and care givers to track the progression of a disease. Forexample, in Alzheimer's disease some people have mild symptoms for manyyears, but others have symptoms that increase dramatically. If thecognitive symptoms can be measured it may give an indication when totake certain precautions such as not allowing the patient to live alone.Monitoring cognitive deficits also allows patients, clinicians, and caregivers to monitor the response to any therapy or intervention,particularly in cases where the intervention is not known to beeffective for an entire population. One example of such an embodiment isthe use of the cognitive assessment tool to monitor the effectiveness ofthe administration of stimulant medications for a patient with attentiondeficit hyperactivity disorder (ADHD). Another use of the tool as acognitive monitor is the observation of the presence and severity of anycognitive side effects from therapies with known cognitive impact, suchas chemotherapy, or therapies with uncharacterized pharmacodynamics. Inpreferred embodiments, the monitoring may be repeated every 30 minutes,every few hours, every day, a few times a week, every week, every otherweek, every month, or every year.

In one embodiment, the cognitive assessment tool may be used tocharacterize the cognitive state of students. When used in schools andtraining programs, the described methods may be used to identifystudents who need special resources, identify students who need furtherneurological evaluation, identify students who would benefit fromcognitive training, place students into the correct difficulty level ofsubject matter, and evaluate the effectiveness of educational curriculumand programs, among other things. The described methods may also be usedto evaluate new curriculum or school programs, particularly thosedesigned to improve cognitive abilities.

In one embodiment, the cognitive assessment tool may be used to assesscognitive abilities to evaluate the user's capabilities of functioningin a high demand job, particularly one that regularly relies on the usermulti-tasking.

In some embodiments, the cognitive assessment tool may be used tomeasure effects of physical and emotional environments on cognitivefunction. The described methods may be used to test the effects ofworkplace environments on employees, surrounds of patients in hospitalsand clinics, level of stress and its cognitive impact on any users, andmuch more.

In some embodiments, the cognitive assessment tool may be used tomeasure the effects of physical environmental exposures on cognitivefunction. In studies or for personal use, this tool may be used tounderstand the impact of chemicals, pollutants, food ingredients, andair quality, among others, on the cognitive function of the user.

In one embodiment, the cognitive assessment tool may be used todetermine if a user has taken substances to alter his or her cognitivestate. For example, this test may be used to screen parolees for druguse and identify those who should be considered for further testing.

Because this cognitive assessment tool may be deployed on multiplecomputer-device platforms, the described methods can be used anywherethere is a computer device. The described cognitive measurement tool hasthe advantage of being able to be useful in a doctor's office, in ahospital, in a school, in a workplace, in a home, in a moving vehicle,outside at a park, while walking down a street, and anywhere a mobiledevice can be carried.

As known to one skilled in the art, the user inputs can be measure onetime, multiple times over a set time period or on a set schedule, or twotimes—specifically before and after a particular change is made, and thenumber of times a user's inputs are measured is determined by thefunction for which the cognitive measurement tool is employed.

Target Populations

Individuals that may benefit from the cognitive assessment tool may beany person. For any of the target populations described below,diagnostics, assessments, or ongoing monitoring tools to assess one'scognitive ability (e.g. impairment or susceptibility to interference)are particularly useful applications of the cognitive assessment tooldescribed herein. It is recognized in the cognitive field thatinterference in cognitive function created by a multi-taskingenvironment may severely impact cognitive performance across a range offunctions, including perception, attention, and memory. Accordingly,there are many potential populations that would benefit from a newmethod that specifically aims to measure cognitive function.

Individuals that can benefit from the subject methods and tools includebut are not limited to adults, such as aging adults. It is well-knownthat healthy aging adults have a significant deficit in processing ofcognitive interference. Additionally, recent findings show the evenyoung adults can show signs of such a deficit (Int. Pat. No.WO2012/064999A1 by Gazzaley, A.). Therefore, adults about 30 years old,or older, can benefit from the methods of the present disclosure.Declines typically accelerate starting at age 50, and worsen oversubsequent decades in a phenomena clinically referred to as “age-relatedcognitive decline.” Such a condition can lead to a more severe ailmentknown as mild cognitive impairment. If the deficit is identified earlycognitive therapeutic steps can be initiated, such as cognitivetraining. Additionally, prevention measures can be introduced in tasksthat require extraction of visual or auditory information whilemulti-tasking or avoiding distraction, such as driving a car.

For individuals suffering from chronic neurological and psychiatricillness, changes in inhibitory neuron populations, myelination, responseslowing, emergent response dis-coordination, degradation of responseselectivity in spatial, spectral and temporal detail, and in thedegradation of the distinctions between background and target stimuliare very similar to the effects of age-related cognitive decline.Accordingly, individuals of any age with profiles of cognitiveimpairment that parallel those in aging are target populations for themethods and tools of the present disclosure.

Aside from aging, measuring cognitive impairment can be useful foridentifying deficits in others at risk. For example, the disclosedcognitive assessment tool may be useful for identifying cognitive lossesthat have arisen as a consequence of injury (e. g. traumatic braininjury), medical treatments, chronic illness, or of unknown cause. Suchcognitive impairment, age-related or not, can be a contributing factoror manifesting symptom of a variety of conditions, including Alzheimer'sdisease, Parkinson's disease, Huntington's disease, depression,schizophrenia, dementia (including, but not limited to, AIDS relateddementia, vascular dementia, age-related dementia, dementia associatedwith Lewy bodies, and idiopathic dementia), Pick's disease, cognitivedeficits associated with fatigue, multiple sclerosis, post-traumaticstress disorder (PTSD), obsessive-compulsive disorder (OCD), and others.Other cognitive losses can include brain damage attributable toinfections pathogens, medical intervention, alcohol, and drugs, amongothers. Additionally, cognitive decline may result as a secondarysymptom from a variety of disease states that are on the surfaceunrelated to cognition, but which significantly adversely affectanxiety, stress, panic, depression, dysphoria, or malaise. Accordingly,individuals experiencing pain or disease having a significant paincomponent, insomnia, or potential adverse effects of disease treatmentsuch as general anesthesia, dialysis, chemo therapy, or radiationtherapy can also benefit from using the cognitive assessment tool.

Populations that can further benefit from the present cognitiveassessment tool further encompass those that suffer from attentiondeficit disorder (e.g. ADHD). Similarly, cognitive losses can becharacterized for developmentally impaired child and adult populations,encompassing general or undiagnosed developmental delays, SensoryProcessing Disorder (SPD), and Autism Spectrum Disorder (ASD).

Assessing specifically cognitive abilities related to performing twotasks at once, as described in this disclosure, may be vital forassessing professional abilities. Professions requiring significantmulti-tasking include, but are not limited to, athletes, airline pilots,military personnel, doctors, call center employees, teachers, anddrivers of vehicles.

Assessing specific cognitive abilities is also useful for people withcurrent or previous substance abuse problems or additions.

Another non-medical population that can benefit from the cognitiveassessment tool are school age children. Assessments of cognitivetraining may be useful in identifying children with special needs or whoshould be targeted for cognitive training and specific educationalprograms.

Demonstration of Efficacy

With the goal to assess cognition and related effects in individuals, itcan be desirable to experimentally determine the accuracy of adiagnosis. Suitable methods of experimental testing include those typesof studies known in the art to test the accuracy of a new cognitivemeasurement, including pilot studies with humans and clinical trials.These types of experimental tests can be conducted with any group ofindividuals, and preferably with a group of individuals that representthe target population of the eventual market products. Preferably, thestudies are conducted in such a way as to give a strong statisticalsupport to the conclusions.

In one embodiment of such a study, the disclosed methods are used tomeasure cognition at the same time as another well characterizedassessment to compare the two results. This assessment can focus ongeneral cognitive functions, which can pertain to both healthyindividuals and individuals that have experienced or are at risk ofexperiencing cognitive deficits, including clinical patient populations.Such suitable tests include those know in the art to test any specificfunctions of a range of cognitions in cognitive or behavioral studies,including tests for perceptive abilities, reaction and other motorfunctions, visual acuity, long-term memory, working memory, short-termmemory, logic, decision making and the like.

In another embodiment of an efficacy study, it can be tested if thedescribed cognitive assessment tool captures known cognitive deficitsthat are associated with aging when tested across a wide range of ages.In such an embodiment other tools can be used in addition to age tocorrelate with function, such as the cognitive assessments known to oneskilled in the art as describe above, or to actual functional activitiesof daily living. Examples of tests that are specifically constructed orvalidated to measures such functional outcomes are, Activities of DailyLiving for elderly populations, or simple measurements such as theability to perform a directed task, read, or comprehend conversation;efficiency in a workplace environment; and the like.

In another embodiment of an efficacy study, the cognitive assessmenttool can be tested for its ability to capture cognitive changes that areassociated with known change agents such as stimulants, depressants, andsleep deprivation. Such a study can also employ known cognitiveassessments described previously for comparison to the disclosed methodsfor assessing cognition.

In another embodiment of an efficacy study, the cognitive assessmenttool can be assessed for its ability to capture known cognitive deficitsassociated with specific disease populations and differentiate severitywithin a diseased population. In such an embodiment, the disclosedmethods for assessing cognition can be compared with other cognitiveevaluations and functional measures described above along with teststhat measure symptoms or functions relevant to a specific disease orcondition. Suitable types of tests include those that objectivelymeasure symptom severity or biomarkers of a disease condition, teststhat use subjective clinician or observer measurement of symptomseverity, tests that use self-reported perception of a subject'scondition, and tests that measure cognitive functions know to becorrelate with disease states. Examples of such tests include, but arenot limited to assessment scales or surveys such as the Mini MentalState Exam, CANTAB cognitive battery, Test of Variables of Attention(TOVA), Repeatable Battery for the Assessment of NeuropsychologicalStatus, Clinical Global Impression scales relevant to specificconditions, Clinician's Interview-Based Impression of Change, SevereImpairment Battery, Alzheimer's Disease Assessment Scale, Positive andNegative Syndrome Scale, Schizophrenia Cognition Rating Scale, ConnersAdult ADHD Rating Scales, Hamilton Rating Scale for Depression, HamiltonAnxiety Scale, Montgomery-Asberg Depressing Rating scale, Young ManiaRating Scale, Children's Depression Rating Scale, Penn State WorryQuestionnaire, Hospital Anxiety and Depression Scale, Aberrant BehaviorChecklist, Activities for Daily Living scales, ADHD self-report scale,Positive and Negative Affect Schedule, Depression Anxiety Stress Scales,Quick Inventory of Depressive Symptomatology, and PTSD Checklist;physiological tests that measure internal markers of disease or healthsuch as detection of amyloid beta, cortisol and other stress responsemarkets; and brain imaging studies (for example fMRI, PET, etc.) thatassess a condition based on the presence of specific neural signatures.

In another embodiment of efficacy studies, the cognitive assessment toolcan be tested for its ability to differentiate between different diseasepopulations that have similar phenotypes. Such a study would useparticipants with known diagnoses for diseases with similar phenotypesand, potentially individuals with no known disease state related tocognitive function. Such a study could also employ the cognitive,functional, and symptom related tests described previously in thissection.

In another embodiment of efficacy studies, the cognitive assessment toolcan be employed multiple times for each person all at once or on a fixedschedule in the study to demonstrate the stability of the cognitivemeasures. Such a study could also give participants cognitive,functional, and symptom related tests described previously in thissection at the same time as the disclosed cognitive measurement tool forcomparison.

In another embodiment of efficacy studies, the cognitive assessment toolcan be employed multiple times for each person all at once or on a fixedschedule while at the same time the user is given a known cognitiveenhancing or cognitive impairing treatment to demonstrate thesensitivity of the measure to the known treatment. Such a study coulduse a neuro-stimulant or caffeine for a cognitive enhancing treatment oralcohol or sleep deprivation for a cognitive impairment treatment. Sucha study could also simultaneously with each use of the describedcognitive measurement tool employ the cognitive, functional, and symptomrelated tests described previously in this section.

EXAMPLES

It is understood that the examples and embodiments described herein arefor illustrative purposes only and that various modifications or changesin light thereof will be suggested by persons skilled in the art and areto be included within the spirit and purview of this application andscope of the appended claims. All publications, patents, and patentapplications cited herein are hereby incorporated by reference in theirentirety for all purposes.

Project: EVO—Computational Cognitive Assessment System

We have designed and built an adaptive cognitive assessment system asthe underlying software mechanics in a clinical prototype cognitiveassessment game entitled “Project: EVO,” which is operated by anindividual on a mobile tablet or smartphone. The adaptive cognitiveassessment system that powers Project: EVO uses the methods ofmeasurements to evaluate the cognitive abilities of the player.

Background of Project: EVO

Project: EVO was built as a mobile video game that can assess theexecutive function of an individual by measuring inputs the individualgives while performing two tasks concurrently (multi-tasking), in anengaging computer environment. To date, the game has been deployed inmultiple clinical studies that use comparisons to standard assessmentsfor cognition, behavioral and symptomatic measurements. Examplescreenshots from the functional clinical version of Project: EVO areshown in FIG. 3 , described above.

Project: EVO presents two types of tasks to an individual: a perceptualreaction task (called “Tapping” in the game) and a visuomotor trackingtask (called “Navigation” in the game). The perceptual reaction taskrequires an individual to respond by tapping on the screen of the mobiletablet/phone when a visual target of interest appears (for example, agreen circular fish) but to inhibit their response and not tap thescreen when a target that is not of interest appears (for example, agreen square-shaped fish or a red circular fish). The visuomotor taskrequires an individual to “navigate” a visual figure/avatar down a riverby subtly tilting the screen of the mobile tablet/phone so as to keepthe avatar in the middle of the river. The individual must avoidobstacles that are generated in the avatar's path in order to succeed.The two tasks are based on the basic framework of the multitaskingparadigm.

Difficulty Levels in Project: EVO

The difficulty level of the tasks the user performs is modified based onuser performance. The difficulty of each task for an individual is madeto increase as an individual performs the task correctly and thedifficulty decreases when an individual fails to perform a taskcorrectly. For the reaction task performing the task correctly isconsidered responding in the proper amount of time to the targets ofinterest and not responding to targets that are not of interest. Performthe reaction task incorrectly is the opposite, responding to a targetthat is not of interest or not responding to targets of interest in theallotted amount of time. The difficulty of the reaction task is modifiedby increasing or decreasing the response time allowed when each targetis presented. The game-level for the reaction tasks is determined by thereaction time window presented to the participant. The navigation taskis considered to be performed correctly when the user avoids the wallsand objects in the avatar's path. Allowing the avatar to collide withwalls and objects is considered incorrectly performing the navigationtask. The difficulty level for an individual of the navigation task ismodified by changing the speed of the avatar moving down the path. Thegame-level for the navigation task is determined by the speed of theavatar moving down the path. Project: EVO adapts the difficulty level ofboth tasks in real-time, in order to keep a user challenged anddetermine the threshold of performance possible by the user. Therefore,the individual's performance on the previous events of gameplaydetermines the exact difficulty of the next event, and the aggregateperformance over an extended period of time generally determines theaverage difficulty level that an individual may be experience at any onetime.

“Worlds” of Project: EVO

The current version of the Project: EVO assessment tool was designedwith four different “Worlds” the tasks take place in. For each of thedifferent worlds there are different graphics, different color schemes,and slightly different perceptual reaction tasks. In some cases all ofthe worlds are used in an assessment and in other cases, only one of theworlds is used as an assessment.

Playing Project: EVO Assessment

Project: EVO is set up so that the game can be played one time ormultiple times over a set time period; for example, once a day for fourdays. The player starts the assessment process by practicing the twotask for a short period of time, 4-12 minutes. A player is motivated toplay to their maximal ability by visual and auditory feedback to everytargeting event and incorrect navigation events. In some cases, theplayer is also rewarded for performing the tasks correctly with “points”that can be used to purchase avatars. After this warm-up period theplayer starts an evaluation phase. The player completes each task byitself (single-tasking) and both tasks simultaneously (multi-tasking)until a threshold level of performance is reached.

Data Recorded in Project: EVO Assessment

While the player is immersed in the multi-tasking phase of the game, theuser's performance measures are recorded. Specifically, the navigationlevel, the tapping level, the reaction time to stimuli in the perceptualreaction task, and whether a user response correctly to an interferencestimuli by tapping or correctly to a distractor stimuli by not tappingthe touch screen. These specific data points are used to calculate othermeasures that represent cognitive measures, such as the thresholdperformance levels, mean performance level, variation in performancelevels, mean reaction time, variation in reaction time, and othercomplex and composite cognitive function variables.

Training Program with Project: EVO

Sometimes, the Project: EVO assessment tool is accompanied by a Project:EVO personalized training program with adaptive rewards (Int. Pat. No.WO2012/064999A1 by Gazzaley, A.; U.S. Pat. Appl. No. 62/001,141 byMartucci, Piper, Omernick, Gazzaley, Elenko, and Karanam). Thepersonalized training program involves practicing the multi-taskingphase of the assessment with encouragement and rewards for performingboth tasks well. The difficulty of the adaptive reward program and thegates that allow a user to progress to the next world is set by theProject: EVO Assessment results. Data for assessment can also begenerated during this training phase.

Pilot Study: Multitasking System Detects Known Cognitive Decline inAging

We conducted a study using our assessment tool for a group of olderadults (between 60 and 75 years old, n=15) and a group of younger adults(ages between 20 and 30 years old, n=19). This study was conducted withan academic partner trained in cognitive assessment methods.Participants had no other known cognitive impairment and had no symptomsof depression. The older adults were also required to have a Mini-MentalState Exam score great than or equal to 27.

Participants in the study were given an evaluation in different EVOworlds within the game. The participants then participated in theProject: EVO cognitive training program, which includes taking theevaluation at least two more times within each world. This process wasrepeated for at least 3 worlds. Participants were given the worlds in arandom order. The participants played at most 7 rounds of the Project:EVO training or assessment per day for 28 days. The initial evaluationwas done in the lab setting under the supervision of the researcher. Allthe remaining sessions were played at home with no guidance orinterference from the research team.

The results present are from just one of the worlds within the Project:EVO game. FIG. 4 presents the results of the assessment studies. Therewas significant difference (p<0.05) between the older adults and theyounger adults for the mean reaction time to stimuli (FIG. 4 .A) and thestandard deviation of the reaction time (FIG. 4 .B) while theparticipants are in a multi-tasking environment. Our cognitivemeasurement tool was able to show the cognitive decline that is known tobe present in older adults.

Pilot Study: Multi-Tasking Measures Differentiate Populations Betterthan Other Cognitive Tests and Single-Tasking Measures

The embodiment described in this disclose would prove greater than stateof the art cognitive measures if it were able to differentiate apopulation that has a well-established risk for neurodevelopmentaldisabilities including autism, those with deletions and duplications atchromosome 16p.11.2 BP4-BP5, from neurotypical age-matched siblingsbetter than other cognitive tests. Potentially, this could be tested atan annual meeting for families of children with this specific disorder.

In such a study, both the 16p.11 carrier children and their siblingswould play a Project: EVO assessment along with Motor Speed and SymbolDigit tests that assess basic motor and processing speed abilities andFlanker and Visual Search tests that assess attention based processingin the presence of a distraction.

The study would prove successful if Project: EVO multi-tasking measuressuch as game level threshold and reaction time while multi-tasking wereable to differentiate between the carrier children and the neurotypicalchildren better than the traditional cognitive tests that are notmulti-tasking.

Pilot Study: Multitasking Measurements May Enable Unique DiseaseSignatures

The disclosed method for measuring cognitive function would be usefulabove the state of the art if the multi-tasking cognitive measures areable to differentiate between different cognitively impairedpopulations, such as Sensory Processing Disorder, Autism SpectrumDisorder, and Attention Deficit Hyperactivity Disorder (ADHD). If datafrom these different populations, which could potentially be collectedthrough different clinical research protocols, demonstrate that thesepopulations are differentiated from the neurotypical cohorts throughdistinct patterns of multi-task measures, this tool would be clinicallyuseful. These patterns in the measurements are unique diseasesignatures, showing the ability our cognitive measurement tool todifferentiate between different cognitively impaired populations, somewith similar cognitive phenotypes with as Autism Spectrum Disorder andSensory Processing Disorder.

Pilot Study: Stability of Multi-Tasking Performance Measures Over Time

The tool described in this disclosure would be clinically useful if themulti-tasking cognitive measures are reliable and stable over time andmultiple uses of the measurement tool. The EVO Assessment tool can beplayed by a user on varying schedules. Such schedules include using thetool once per day, using the tool multiple times spread out over theday, using the tool a few times per week, and using the tool multipletimes per week. Within a specific user or within a group of users on asimilar schedule, the stability can be evaluated by calculating theInterclass Correlation Coefficient (ICC) ICC scores greater than 0.70indicate good reliability. The ICC can be computed for both neurotypicalpopulations and populations with known illness or cognitive impairment.

Pilot Study: Sensitivity of Multi-Tasking Cognitive Measurements toPharmacologic Agents or Circadian Rhythm

The tool described in the disclosure would be considered a sensitivecognitive measurement if the measurement changes outside of expectedvariation when the person using the tool has taken a cognitive enhancingagent (such as methylphenidate) or a cognitively detrimental agent (suchas triazolam). Sensitivity to these agents could be tested by havingparticipants take a placebo, methylphenidate, and triazolam in a randomand unknown order. If the performance on the multi-tasking test changesfrom before to after the drugs, the tool is sensitive to cognitivefunction changes.

Additionally, without using cognitive agents, there is known subtleincrease and decrease in cognitive function due to circadian rhythm andtime awake. If the multi-tasking cognitive measures described in thisdisclosure are sensitive to such circadian rhythms or extended periodsof time awake, as defined by the ability to detect such a statestatistically, it would be a marker of sensitive cognitive measurement.

Measurement of Multi-Tasking Performance During Commercial Video GamePlay as a Cognitive Assessment

The described cognitive measurement methods are embodied by themeasurement of user inputs while the user is engaged in multi-tasking ina commercially available video game on a gaming console. During thevideo game the user explores a “world” created in the video game to lookfor enemy soldiers. When the user identifies an enemy soldier the userattempts to shoot the soldier. At times, the user can be engaged in bothmoving around the world and shooting a target (the enemy soldier). Whenthe user is engaged in both of these tasks, the user is multi-tasking.Data from user inputs are extracted for any moment in which the user isengaged in multi-tasking during a session of game play. The user inputdata is analyzed by a computer device to determine the accuracy of thetargeting, accuracy of targeting under increased difficult (speed of amoving target), the pace at which the user is moving, the number oftimes a user creates an error in the navigation of the video game world(e.g. running into an obstacle), the pattern of movement whilenavigating the world (e.g. the number of times the avatar needs to backtrack), and other performance measures. Standard statistical summarymethods are also computed to represent cognitive measures. Dataregarding the user's performance is outputted by the computer device asan indications of the user's current cognitive assessment. After aperformance baseline is established, passive monitoring in this engagemanner of video game play can be used to assess the level of sleepdeprivation for healthy individuals or to monitor changes in cognitivefunction after a medical intervention such as stimulant therapy for ayoung adult with ADHD.

Measurement of Multi-Tasking Performance During Written Communicationsas a Cognitive Assessment

The disclosed cognitive assessment tool may be embodied by themeasurement of keyboard user inputs on a laptop or desktop computerwhile a user is engaged in written communication and instant messaging.During the use of a computer for recreational or work related purposes,a user is often engaged in writing a letter, report, or email while atthe same time responding to instant messages. The instances in which auser is actively writing in a word document or email, determined as thetime within 30 seconds of active typing, and actively responding toinstant messaging, determined as the time when a user engages in aninstant message communication window and types a response on thekeyboard, the user is determined to be multi-tasking. During periods ofmulti-tasking, the user inputs to the computer device are extracted.This data is used to evaluate the typing speed, typing accuracy (asdetermined by the number of misspelled words, the number of times a usermust delete text, or a composite of the two), the processing time (timewith an active instant messaging, email, or document window activatedbefore typing is started or percent time with an active instantmessaging, email, or document window activated during which no typing isoccurring), reaction time to the alert that a new instant message hasbeen received, and other performance measures. These data are also beused to create complex measures of cognitive function. Data regardingthe user's performance is outputted by the computer device as anindication of a user's current cognitive assessment. After a performancebaseline is established, the cognitive assessment tool is used byemployers to establish an ideal workplace for an employee and used byschools as a passive screen to identify students who may need cognitivetraining or further cognitive testing for attention or sensorydisorders.

Aspects of the present disclosed methods are described above withreference to flowchart illustrations and/or block diagrams of methods,apparatus (systems) and computer program products according toembodiments of the invention. It will be understood that each block ofthe flowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

FIG. 5 is a schematic block diagrams of an example network 500 andcomputing devices that may be used (or components thereof) with one ormore embodiments described herein, e.g., as one of the nodes shown inthe network 500. In one embodiment, a computer processing system forassessing cognitive abilities may be a single mobile device 501. Forexample, the mobile device 501 may present the tasks to the individualuser, receive responses from the individual, determine that the tasksare performed by the individual (the user is multi-tasking), determineperformance measures using the responses, computing a cognitive measureusing the performance measures, and output a cognitive abilityassessment. In another embodiment, a computer processing system forassessing cognitive condition may be a distributed computing systemincluding several processing units. For example, a mobile device 503 maypresent the tasks to the individual, receive responses from theindividual, and transmit the responses through communication network 500to a server 506 for subsequent processing. The server 506 may receivethe response, determine that the tasks are performed by the individual,determine performance measures using the responses, compute a cognitivemeasure using the performance measures, and output a cognitive abilityassessment to the mobile device 503 for display. In another embodiment,the performance measures may be determined by the mobile device 503rather than by the server 506. Other division of labor among distributedprocessing components (which may be more than 2) are within the prevueof one of ordinary skill in the art.

FIG. 6 is a block diagram of an exemplary computer processing system orcomputing device 600. The depicted system is only one example of asuitable system and is not intended to suggest any limitation as to thescope of use or functionality of embodiments of the invention describedherein. In one embodiment, the system 600 includes a processing unit 616(e.g., a CPU, GPU, etc.). The processing unit 616 may access and writeto memory 628 through bus 618. Memory 628 may include, e.g., RandomAccess Memory (RAM) 630, cache 632, and storage system 634 (e.g., harddrive, flash drive, DVD drive, etc.). Within memory 628, a filestructure 640 may be implemented to store and provide access to filesand data 615. The processing unit 616 may also be in communication witha network adapter 620 that enables the system 600 to communicate withother devices in a network. Examples of network adapters 620 include,e.g., Ethernet adapters, Wi-Fi wireless adapters, and cellular networkadapters. Further, the processing unit 616 may output and receive datathrough an input/output interface (I/O interface) 622, which may enablesystem 600 to output data to or receive data from external devices(e.g., mouse, keyboard, CD drive, etc.) 614 and displays (e.g.,monitors, touch screen, etc.) 624.

With certain illustrated embodiments described above, it is to beappreciated that various non-limiting embodiments described herein maybe used separately, combined or selectively combined for specificapplications. Further, some of the various features of the abovenon-limiting embodiments may be used without the corresponding use ofother described features. The foregoing description should therefore beconsidered as merely illustrative of the principles, teachings andexemplary embodiments of this invention, and not in limitation thereof.

It is to be understood that the above-described arrangements are onlyillustrative of the application of the principles of the illustratedembodiments. Numerous modifications and alternative arrangements may bedevised by those skilled in the art without departing from the scope ofthe illustrated embodiments, and the appended claims are intended tocover such modifications and arrangements.

What is claimed is:
 1. A computer-implemented method comprising:receiving, by a computer processing system, a first plurality ofresponses by an individual to a first task, the first task comprising afirst stimuli configured to evoke the first plurality of responses fromthe individual over a period of time; receiving, by the computerprocessing system, a second plurality of responses by the individual toa second task, the second task comprising a second stimuli configured toevoke the second plurality of responses from the individual over theperiod of time, wherein the second stimuli are presented simultaneouslywith at least some of the first stimuli; computing, by the computerprocessing system, a cognitive measure using a combination of one orboth of the first plurality of responses and the second plurality ofresponses and external information, wherein computing the cognitivemeasure comprises: (i) determining at least one performance measureusing one or both of the first plurality of responses and the secondplurality of responses, the at least one performance measure beingassociated with one or more cognitive functions associated with one ormore specific diseases or disease states, (ii) comparing the at leastone performance measure to the external information, the externalinformation comprising one or more performance measure of individualswith known cognitive conditions associated with the one or more specificdiseases or disease states, and (iii) applying a computer data modelcomprising at least one machine learning technique to the performancemeasures, wherein the at least one machine learning technique isconfigured to analyze one or more stimulus-response patterns from thefirst plurality of responses and the second plurality of responses tocompute a predictive measure for the one or more specific diseases ordisease states; and outputting, by the computer processing system, acognitive assessment for the individual based on the cognitive measure,wherein one or both of the first plurality of responses and the secondplurality of responses (i) comprise at least one of motion by theindividual or physiological input from the individual, and (ii) aredetected using one or more sensors, the sensors being selected from thegroup consisting of an accelerometer, a gyroscope, and a physiologicalsensor.
 2. The computer-implemented method of claim 1, wherein the firsttask is a visuomotor task, the first stimuli include a navigation path,and the first plurality of responses include continuous inputs.
 3. Thecomputer-implemented method of claim 1, wherein the second task is areaction task, the second stimuli include target stimuli that requireresponses from the individual, and the second plurality of responsesinclude inputs reacting to the target stimuli.
 4. Thecomputer-implemented method of claim 3 wherein the second stimuliinclude distractor stimuli that require no response from the individual.5. The computer-implemented method of claim 1 wherein computing thecognitive measure comprises computing one or more of a hit rate, falsealarm rate, correct response rate, and miss rate.
 6. Thecomputer-implemented method of claim 1 further comprising modifying,during the period of time, a difficulty level of the first task or thesecond task based on performance measures.
 7. The computer-implementedmethod of claim 6 wherein the difficulty level is selected from thegroup consisting of: allowable reaction time window for reacting tostimuli, navigation speed, number of obstacles, size of obstacles,frequency of turns in a navigation path, and turning radiuses of turnsin a navigation path.
 8. The computer-implemented method of claim 1further comprising: modifying, during the period of time, a firstdifficulty level of the first task based on performance measures of oneor both of the first plurality of responses and the second plurality ofresponses; and modifying, during the period of time, a second difficultylevel of the second task based on performance measures of one or both ofthe first plurality of responses and the second plurality of responses.9. The computer-implemented method of claim 8 wherein the cognitivemeasure is computed using one or both of the first difficulty levelmodifications and the second difficulty level modifications.
 10. Acomputer-implemented system comprising: one or more processors; and anon-transitory computer-readable memory comprising instructions storedthereon that, when executed, cause the one or more processors to executeoperations comprising: receiving a first plurality of responses by anindividual to a first task, the first task comprising a first stimuliconfigured to evoke the first plurality of responses from the individualover a period of time; receiving a second plurality of responses by theindividual to a second task, the second task comprising a second stimuliconfigured to evoke the second plurality of responses from theindividual over the period of time, wherein the second stimuli arepresented simultaneously with at least some of the first stimuli;computing a cognitive measure using a combination of one or both of thefirst plurality of responses and the second plurality of responses andexternal information, wherein computing the cognitive measure comprises:(i) determining at least one performance measure using one or both ofthe first plurality of responses and the second plurality of responses,the at least one performance measure being associated with the one ormore cognitive functions associated with one or more specific diseasesor disease states, (ii) comparing the at least one performance measureto the external information, the external information comprising one ormore performance measure of individuals with known cognitive conditionsassociated with the one or more specific diseases or disease states, and(iii) applying a computer data model comprising at least one machinelearning technique to the performance measures, wherein the at least onemachine learning technique is configured to analyze one or morestimulus-response patterns from the first plurality of responses and thesecond plurality of responses to compute a predictive measure for theone or more specific diseases or disease states; and outputting acognitive assessment for the individual based on the cognitive measure,wherein one or both of the first plurality of responses and the secondplurality of responses (i) comprise at least one of motion by theindividual or physiological input from the individual, and (ii) aredetected using one or more sensors, the sensors being selected from thegroup consisting of an accelerometer, a gyroscope, and a physiologicalsensor.
 11. The computer-implemented system of claim 10 wherein thefirst task is a visuomotor task, the first stimuli include a navigationpath, and the first plurality of responses include continuous inputs.12. The computer-implemented system of claim 11 wherein the operationsfurther comprise modifying, during the period of time, a seconddifficulty level of the second task based on performance measures of oneor both of the first plurality of responses and the second plurality ofresponses.
 13. The computer-implemented system of claim 12 wherein thefirst difficulty level and the second difficulty level are modified inreal-time during the period of time.
 14. The computer-implemented systemof claim 13 wherein the cognitive measure is computed using one or bothof the first difficulty level modifications and the second difficultylevel modifications.
 15. The computer-implemented system of claim 10wherein the second task is a reaction task, the second stimuli includetarget stimuli that require responses from the individual, and thesecond plurality of responses include inputs reacting to the targetstimuli.
 16. The computer-implemented system of claim 15 wherein thesecond stimuli include distractor stimuli that require no response fromthe individual.
 17. The computer-implemented system of claim 10 whereincomputing the cognitive measure comprises computing one or more of a hitrate, false alarm rate, correct response rate, and miss rate.
 18. Thecomputer-implemented system of claim 10 wherein computing the cognitivemeasure includes applying a signal detection technique selected from thegroup consisting of: sensitivity index, receiver operatingcharacteristics (ROC), and bias.
 19. The computer-implemented system ofclaim 10 wherein the operations further comprise modifying, during theperiod of time, a first difficulty level of the first task based onperformance measures of one or both of the first plurality of responsesand the second plurality of responses.
 20. A non-transitorycomputer-readable medium encoded with instructions thereon that, whenexecuted by a computer processing system, cause the computer processingsystem to perform one or more operations, the one or more operationscomprising: receiving a first plurality of responses by an individual toa first task, the first task comprising a first stimuli configured toevoke the first plurality of responses from the individual over a periodof time; receiving a second plurality of responses by the individual toa second task, the second task comprising a second stimuli configured toevoke the second plurality of responses from the individual over theperiod of time, wherein the second stimuli are presented simultaneouslywith at least some of the first stimuli; computing a cognitive measureusing a combination of one or both of the first plurality of responsesand the second plurality of responses and external information, whereincomputing the cognitive measure comprises: (i) determining at least oneperformance measure using one or both of the first plurality ofresponses and the second plurality of responses, the at least oneperformance measure being associated with the one or more cognitivefunctions associated with one or more specific diseases or diseasestates, (ii) comparing the at least one performance measure to theexternal information, the external information comprising one or moreperformance measure of individuals with known cognitive conditionsassociated with the one or more specific diseases or disease states, and(iii) applying a computer data model comprising at least one machinelearning technique to the performance measures, wherein the at least onemachine learning technique is configured to analyze one or morestimulus-response patterns from the first plurality of responses and thesecond plurality of responses to compute a predictive measure for theone or more specific diseases or disease states; and outputting acognitive assessment for the individual based on the cognitive measure,wherein one or both of the first plurality of responses and the secondplurality of responses (i) comprise at least one of motion by theindividual or physiological input from the individual, and (ii) aredetected using one or more sensors, the sensors being selected from thegroup consisting of an accelerometer, a gyroscope, and a physiologicalsensor.